Aspects of designing and evaluating seasonal-to-interannual Arctic sea-ice prediction systems

Article English OPEN
Hawkins, Ed ; Tietsche, Steffen ; Day, Jonathan J. ; Melia, Nathanael ; Haines, Keith ; Keeley, Sarah (2016)

Using lessons from idealised predictability experiments, we discuss some issues and perspectives on the design of operational seasonal to inter-annual Arctic sea-ice prediction systems. We first review the opportunities to use a hierarchy of different types of experiment to learn about the predictability of Arctic climate. We also examine key issues for ensemble system design, such as: measuring skill, the role of ensemble size and generation of ensemble members. When assessing the potential skill of a set of prediction experiments, using more than one metric is essential as different choices can significantly alter conclusions about the presence or lack of skill. We find that increasing both the number of hindcasts and ensemble size is important for reliably assessing the correlation and expected error in forecasts. For other metrics, such as dispersion, increasing ensemble size is most important. Probabilistic measures of skill can also provide useful information about the reliability of forecasts. In addition, various methods for generating the different ensemble members are tested. The range of techniques can produce surprisingly different ensemble spread characteristics. The lessons learnt should help inform the design of future operational prediction systems.
  • References (29)
    29 references, page 1 of 3

    JM, Keen AB, Pardaens AK, Lowe JA, Bodas-Salcedo A, Stark S, Searl Y. 2006. The new Hadley Centre Climate Model (HadGEM1): Evaluation of coupled simulations. J. Climate 19: 1327-1353, doi:10.1175/JCLI3712.1.

    Jolliffe IT, Stephenson DB (eds). 2012. Forecast Verification: A Practicioner's Guide in Atmospheric Science. Wiley-Blackwell.

    Jung T, Kasper MA, Semmler T, Serrar S. 2014. Arctic influence on subseasonal midlatitude prediction. Geophysical Research Letters 41(10): 3676-3680, doi:10.1002/2014GL059961.

    Juricke S, Lemke P, Timmermann R, Rackow T. 2013. Effects of stochastic ice strength perturbation on arctic finite element sea ice modeling. J. Climate 26: 3785-3802, doi:10.1175/JCLI-D-12-00388.1.

    Koenigk T, Mikolajewicz U. 2009. Seasonal to interannual climate predictability in mid and high northern latitudes in a global coupled model. Climate Dynamics 32: 783-798, doi:10.1007/s00382-008-0419-1.

    Kumar A, Hoerling MP, Barnston AG. 2001. Seasonal Predictions, Probabilistic Verifications, and Ensemble Size. doi:10.1175/1520- e 0442(2001)014¡1671:SPPVAE¿2.0.CO;2.

    Lindsay RW, Zhang J, Schweiger AJ, Steele MA. 2008. Seasonal l predictions of ice extent in the arctic ocean. JGR: Oceans 113, doi: 10.1029/2007JC004259.

    Liu C, Haines K, Iwi A, Smith D. 2012. Comparing the UK Met Office Climate PredicctionSystem (DePreSys) with idealized predictability in the HadCM3 model. QJRMS 138: 81-90, doi:10.1002/qj.904.

    MerryfieildWJ, Lee WS, Wang W, Chen M, Kumar A. 2013. Multi-system seasonal predictions of Arctic sea ice. Geophysical Research Letters 40: 1551-t1556, doi:10.1002/grl.50317.

    Msadek R, Vecchi GA, Winton M, Gudgel RG. 2014. Importance of initial conditrions in seasonal predictions of arctic sea ice extent. Geophysical Research Letters 41: 5208-5215, doi:10.1002/2014GL060799.

  • Metrics
    views in OpenAIRE
    views in local repository
    downloads in local repository

    The information is available from the following content providers:

    From Number Of Views Number Of Downloads
    Central Archive at the University of Reading - IRUS-UK 0 99
Share - Bookmark